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Record W2415267428

Damage and failure prediction in Alumina Tri-Hydrate/Epoxy core composite sandwich panels subjected to impact loads

2016· article· en· W2415267428 on OpenAlexaff
G. Morada, Aymen Marouene, Rim Ouadday, Aurélian Vadean, Rachid Boukhili

Bibliographic record

VenueThe 7th International Conference on Computational Methods (ICCM2016) · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsEpoxyComposite materialMaterials scienceComposite numberCore (optical fiber)CrimpSandwich-structured compositeDeformation (meteorology)DissipationStructural engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

This paper reports an experimental and numerical analysis of the impact behavior of composite sandwich panels. An innovative sandwich construction with an ATH/Epoxy core (i.e. epoxy resin filled with alumina tri-hydrate (ATH) particles) and non-crimp glass fabric fibre-reinforced epoxy face-sheets was subjected to impact loads. Explicit nonlinear finite elements model was developed to predict the damage characteristics in both the face-sheets and core. The obtained numerical results were compared with the test data to assess the effectiveness of the proposed model. A good correlation with respect to the contact force and energy-time relationships, permanent deformation, and impact-induced damage was achieved. The contribution of each component of the sandwich structure to its energy absorption capabilities was also evaluated. It was found, for an impact energy of 21J, that the energy dissipated in the ATH/Epoxy core is almost two times more than that dissipated in the face-sheets. The important role of the core material for reducing face-sheet damage was identified.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.371
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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